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Graph mining plays a pivotal role across a number of disciplines, and avariety of algorithms have been developed to answer who/what type questions.For example, what items shall we recommend to a given user on an e-commerceplatform? The answers to such questions are typically returned in the form of aranked list, and graph-based ranking methods are widely used in industrialinformation retrieval settings. However, these ranking algorithms have avariety of sensitivities, and even small changes in rank can lead to vastreductions in product sales and page hits. As such, there is a need for toolsand methods that can help model developers and analysts explore thesensitivities of graph ranking algorithms with respect to perturbations withinthe graph structure. In this paper, we present a visual analytics framework forexplaining and exploring the sensitivity of any graph-based ranking algorithmby performing perturbation-based what-if analysis. We demonstrate our frameworkthrough three case studies inspecting the sensitivity of two classicgraph-based ranking algorithms (PageRank and HITS) as applied to rankings inpolitical news media and social networks.

Auditing the Sensitivity of Graph-based Ranking with Visual Analytics